"""simple docstring"""



import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class SCREAMING_SNAKE_CASE  (     snake_case__						):







 """simple docstring"""


 a_							:						List[Any]	      =['''image_processor''', '''tokenizer''']
 a_							:						Dict	      ='''FlavaImageProcessor'''
 a_							:						Dict	      =('''BertTokenizer''', '''BertTokenizerFast''')


 def __init__( self		:   List[str]       , _snake_case		:   Any=None       , _snake_case		:   Union[str, Any]=None       , **_snake_case		:   Union[str, Any]					)					->      Dict:




    '''simple docstring'''






    a__  					=		None
    if "feature_extractor" in kwargs:
       warnings.warn(
           'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
           ' instead.'       , _UpperCAmelCase       , )
       a__  					=		kwargs.pop('feature_extractor'					)

    a__  					=		image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
       raise ValueError('You need to specify an `image_processor`.'					)
    if tokenizer is None:
       raise ValueError('You need to specify a `tokenizer`.'					)

    super().__init__(_UpperCAmelCase       , _UpperCAmelCase					)
    a__  					=		self.image_processor


 def __call__( self		:   List[Any]       , _snake_case		:   List[Any] = None       , _snake_case		:   Optional[int] = None       , _snake_case		:   Optional[Any] = True       , _snake_case		:   List[Any] = False       , _snake_case		:   Dict = False       , _snake_case		:   Optional[Any] = None       , _snake_case		:   Optional[Any] = 0       , _snake_case		:   Optional[Any] = None       , _snake_case		:   int = None       , _snake_case		:   List[str] = None       , _snake_case		:   List[str] = None       , _snake_case		:   Optional[int] = None       , _snake_case		:   Dict = False       , _snake_case		:   Any = False       , _snake_case		:   Dict = False       , _snake_case		:   Tuple = False       , _snake_case		:   str = True       , _snake_case		:   int = None       , **_snake_case		:   int       , )					->      Dict:




    '''simple docstring'''






    if text is None and images is None:
       raise ValueError('You have to specify either text or images. Both cannot be none.'					)

    if text is not None:
       a__  					=		self.tokenizer(
           text=_UpperCAmelCase       , add_special_tokens=_UpperCAmelCase       , padding=_UpperCAmelCase       , truncation=_UpperCAmelCase       , max_length=_UpperCAmelCase       , stride=_UpperCAmelCase       , pad_to_multiple_of=_UpperCAmelCase       , return_token_type_ids=_UpperCAmelCase       , return_attention_mask=_UpperCAmelCase       , return_overflowing_tokens=_UpperCAmelCase       , return_special_tokens_mask=_UpperCAmelCase       , return_offsets_mapping=_UpperCAmelCase       , return_length=_UpperCAmelCase       , verbose=_UpperCAmelCase       , return_tensors=_UpperCAmelCase       , **_UpperCAmelCase       , )
    if images is not None:
       a__  					=		self.image_processor(
           _UpperCAmelCase       , return_image_mask=_UpperCAmelCase       , return_codebook_pixels=_UpperCAmelCase       , return_tensors=_UpperCAmelCase       , **_UpperCAmelCase       , )

    if text is not None and images is not None:
       encoding.update(_UpperCAmelCase					)
       return encoding
    elif text is not None:
       return encoding
    else:
       return BatchEncoding(data=dict(**_UpperCAmelCase					)       , tensor_type=_UpperCAmelCase					)


 def 							_lowerCAmelCase					( self		:   Tuple       , *_snake_case		:   Dict       , **_snake_case		:   Union[str, Any]					)					->      Optional[Any]:




    '''simple docstring'''






    return self.tokenizer.batch_decode(*_UpperCAmelCase       , **_UpperCAmelCase					)


 def 							_lowerCAmelCase					( self		:   Union[str, Any]       , *_snake_case		:   Optional[Any]       , **_snake_case		:   List[Any]					)					->      Optional[Any]:




    '''simple docstring'''






    return self.tokenizer.decode(*_UpperCAmelCase       , **_UpperCAmelCase					)


 @property
 def 							_lowerCAmelCase					( self		:   Optional[Any]					)					->      Tuple:




    '''simple docstring'''






    a__  					=		self.tokenizer.model_input_names
    a__  					=		self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)


 @property
 def 							_lowerCAmelCase					( self		:   List[Any]					)					->      List[str]:




    '''simple docstring'''






    warnings.warn(
        '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       , _UpperCAmelCase       , )
    return self.image_processor_class



 @property
 def 							_lowerCAmelCase					( self		:   str					)					->      str:




    '''simple docstring'''






    warnings.warn(
        '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       , _UpperCAmelCase       , )
    return self.image_processor







'''simple docstring'''

from timeit import timeit






def 				a  (     UpperCamelCase_    :    int			)       ->	int:
		if number < 0:
				raise ValueError('the value of input must not be negative'			)
		snake_case__	     =0
		while number:
				number &= number - 1
				result += 1
		return result






def 				a  (     UpperCamelCase_    :    int			)       ->	int:
		if number < 0:
				raise ValueError('the value of input must not be negative'			)
		snake_case__	     =0
		while number:
				if number % 2 == 1:
						result += 1
				number >>= 1
		return result






def 				a  (     )       ->	None:

		def do_benchmark(UpperCamelCase_    :    int			) -> None:
				snake_case__	     ='import __main__ as z'
				print(f"""Benchmark when {number = }:"""			)
				print(f"""{get_set_bits_count_using_modulo_operator(UpperCamelCase_			) = }"""			)
				snake_case__	     =timeit('z.get_set_bits_count_using_modulo_operator(25)'      ,					setup=UpperCamelCase_			)
				print(f"""timeit() runs in {timing} seconds"""			)
				print(f"""{get_set_bits_count_using_brian_kernighans_algorithm(UpperCamelCase_			) = }"""			)
				snake_case__	     =timeit(
				    'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'      ,					setup=UpperCamelCase_      ,					)
				print(f"""timeit() runs in {timing} seconds"""			)

		for number in (25, 37, 58, 0):
				do_benchmark(UpperCamelCase_			)
				print()


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			benchmark()




